# Deskew

Note: Skew is measured in degrees. Deskewing is a process whereby skew is removed by rotating an image by the same amount as its skew but in the opposite direction. This results in a horizontally and vertically aligned image where the text runs across the page rather than at an angle.

The return angle is between -45 and 45 degrees to don't arbitrary change the image orientation.

By using the library you can set the argument `angle_pm_90` to `True` to have an angle between -90 and 90 degrees.

## Skew detection and correction in images containing text

| Image with skew                                      | Image after deskew                                                 |
| ---------------------------------------------------- | ------------------------------------------------------------------ |
| ![Image with skew](doc/input.jpeg 'Image with skew') | ![Image after deskew](doc/sample_output.jpeg 'Image after deskew') |

## Installation

You can install deskew directly from pypi directly using the following comment

```bash
python3 -m pip install deskew
```

Or to upgrade to newer version

```bash
python3 -m pip install -U deskew
```

## Cli usage

Get the skew angle:

```bash
deskew input.png
```

Deskew an image:

```bash
deskew --output output.png input.png
```

## Lib usage

With scikit-image:

```python
import numpy as np
from skimage import io
from skimage.color import rgb2gray
from skimage.transform import rotate

from deskew import determine_skew

image = io.imread("input.png")
grayscale = rgb2gray(image)
angle = determine_skew(grayscale)
rotated = rotate(image, angle, resize=True) * 255
io.imsave("output.png", rotated.astype(np.uint8))
```

With OpenCV:

```python
import math
from typing import Tuple, Union

import cv2
import numpy as np

from deskew import determine_skew


def rotate(image: np.ndarray, angle: float, background: Union[int, Tuple[int, int, int]]) -> np.ndarray:
    old_width, old_height = image.shape[:2]
    angle_radian = math.radians(angle)
    width = abs(np.sin(angle_radian) * old_height) + abs(np.cos(angle_radian) * old_width)
    height = abs(np.sin(angle_radian) * old_width) + abs(np.cos(angle_radian) * old_height)

    image_center = tuple(np.array(image.shape[1::-1]) / 2)
    rot_mat = cv2.getRotationMatrix2D(image_center, angle, 1.0)
    rot_mat[1, 2] += (width - old_width) / 2
    rot_mat[0, 2] += (height - old_height) / 2
    return cv2.warpAffine(image, rot_mat, (int(round(height)), int(round(width))), borderValue=background)


image = cv2.imread("input.png")
grayscale = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
angle = determine_skew(grayscale)
rotated = rotate(image, angle, (0, 0, 0))
cv2.imwrite("output.png", rotated)
```

## Debug images

If you get wrong skew angle you can generate debug images, that can help you to tune the skewing detection.

If you install deskew with `pip install deskew[debug_images]` you can get some debug images used for
the skew detection with the function `determine_skew_debug_images`.

To start the investigation you should first increase the `num_peaks` (default `20`) and use
the `determine_skew_debug_images` function.

Then you can try to tune the following arguments `num_peaks`, `angle_pm_90`, `min_angle`, `max_angle`,
`min_deviation` and eventually `sigma`.

Inspired by Alyn: https://github.com/kakul/Alyn

## Contributing

Install the pre-commit hooks:

```bash
pip install pre-commit
pre-commit install --allow-missing-config
```